Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/189971
Title: Parallel Motion Planning Using Poisson-Disk Sampling
Authors: Chonhyon Park;Jia Pan;Dinesh Manocha
Year: 2017
Publisher: IEEE
Abstract: We present a rapidly exploring-random-tree-based parallel motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. The Poisson-disk sampling results in improved parallel performance and we highlight the performance benefits on multicore central processing units as well as manycore graphics processing units on different benchmarks.
URI: http://localhost/handle/Hannan/189971
volume: 33
issue: 2
More Information: 359,
371
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7790881.pdf1.23 MBAdobe PDF
Title: Parallel Motion Planning Using Poisson-Disk Sampling
Authors: Chonhyon Park;Jia Pan;Dinesh Manocha
Year: 2017
Publisher: IEEE
Abstract: We present a rapidly exploring-random-tree-based parallel motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. The Poisson-disk sampling results in improved parallel performance and we highlight the performance benefits on multicore central processing units as well as manycore graphics processing units on different benchmarks.
URI: http://localhost/handle/Hannan/189971
volume: 33
issue: 2
More Information: 359,
371
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7790881.pdf1.23 MBAdobe PDF
Title: Parallel Motion Planning Using Poisson-Disk Sampling
Authors: Chonhyon Park;Jia Pan;Dinesh Manocha
Year: 2017
Publisher: IEEE
Abstract: We present a rapidly exploring-random-tree-based parallel motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. The Poisson-disk sampling results in improved parallel performance and we highlight the performance benefits on multicore central processing units as well as manycore graphics processing units on different benchmarks.
URI: http://localhost/handle/Hannan/189971
volume: 33
issue: 2
More Information: 359,
371
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7790881.pdf1.23 MBAdobe PDF